公开(公告)号：US09412075B2

公开(公告)日：2016-08-09

申请号：US13975239

申请日：2013-08-23

Applicant: VMware, Inc.

Inventor： Pradeep Padala ,  Aashish Parikh ,  Zhefu Jiang

IPC: G06F15/18 ,  G06N99/00

CPC classification number: G06N99/005

Abstract: A module and method for automatically scaling a multi-tier application, wherein each tier of the multi-tier application is supported by at least one virtual machine, selects one of reinforced learning and heuristic operation based on a policy to recommend a scaling action from a current state of the multi-tier application. If reinforced learning is selected, the reinforced learning is applied to select the scaling action from a plurality of possible actions for the multi-tier application in the current state. If heuristic operation is selected, the heuristic operation is applied to select the scaling action using a plurality of defined heuristics.

Abstract translation: 一种用于自动缩放多层应用程序的模块和方法，其中所述多层应用程序的每个层由至少一个虚拟机支持，基于策略选择加强学习和启发式操作之一，以推荐来自 当前状态的多层应用程序。 如果选择加强学习，则应用加强学习来从当前状态下的多层应用程序的多个可能的动作中选择缩放动作。 如果选择启发式操作，则应用启发式操作以使用多个定义的启发式来选择缩放操作。

公开(公告)号：US20150058265A1

公开(公告)日：2015-02-26

申请号：US13975239

申请日：2013-08-23

Applicant: VMware, Inc.

Inventor： Pradeep Padala ,  Aashish Parikh ,  Zhefu Jiang

IPC: G06N99/00

CPC classification number: G06N99/005

Abstract: A module and method for automatically scaling a multi-tier application, wherein each tier of the multi-tier application is supported by at least one virtual machine, selects one of reinforced learning and heuristic operation based on a policy to recommend a scaling action from a current state of the multi-tier application. If reinforced learning is selected, the reinforced learning is applied to select the scaling action from a plurality of possible actions for the multi-tier application in the current state. If heuristic operation is selected, the heuristic operation is applied to select the scaling action using a plurality of defined heuristics.

Abstract translation: 一种用于自动缩放多层应用程序的模块和方法，其中所述多层应用程序的每个层由至少一个虚拟机支持，基于策略选择加强学习和启发式操作之一，以推荐来自 当前状态的多层应用程序。 如果选择加强学习，则应用加强学习来从当前状态下的多层应用程序的多个可能的动作中选择缩放动作。 如果选择启发式操作，则应用启发式操作以使用多个定义的启发式来选择缩放操作。